Medical image segmentation is an indispensable process in viewing and measuring various structures in the brain. However, medical images are inherently low contrast, vague boundaries, and high correlative. The traditional fuzzy c-means (FCM) clustering algorithm considers only the pixel attributes. This leads to accuracy degradation with image segmentation. To solve this problem, this paper proposes a robust segmentation technique, called a Generalized Spatial Fuzzy C-Means (GSFCM) algorithm, that utilizes both given pixel attributes and the spatial local information which is weighted correspondingly to neighbor elements based on their distance attributes. This improves the segmentation performance dramatically. Experimental results with several magnetic resonance (MR) images show that the proposed GSFCM algorithm outperforms the traditional FCM algorithms in the various cluster validity functions.
|Title of host publication||IEEE International Conference on Fuzzy Systems, 2009 : FUZZ-IEEE 2009|
|Publication status||Published - 2009|
|Event||2009 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) - Jeju Island, Korea, Republic of|
Duration: 20 Aug 2009 → 24 Aug 2009
|Conference||2009 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)|
|Country||Korea, Republic of|
|Period||20/08/2009 → 24/08/2009|